1. It seems the example is just for a demo purpose, but can be easily
extended to production style code.
Incorporating cross-validation does not take much efforts. For Ex.
Consider Handwritten digits data, keep all digits data except one in
training sample,
perform cross validation for various values of nu and gamma. Choose best of
the parameters for given performance metric.
Then,test on the data of digit which was not included in training dataset.
2. The example is essentially to demonstrate ability to detect outliers in
some kind of bivariate Gaussian distribution.
But again extending it to multivariate Gaussian is not difficult.
On Mon, Aug 25, 2014 at 11:08 PM, Pagliari, Roberto <[email protected]
> wrote:
> w.r.t. the one-class SVM example
>
>
>
>
> http://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html#example-svm-plot-oneclass-py
>
>
>
> is there a reason why cross-validation is not used?
>
>
>
> Also, is novelty detection with multivariate Gaussian distribution
> available in sklearn?
>
>
>
>
>
> Thank you,
>
>
>
>
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--
Warm Regards
Yogesh Karpate
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